Luminal-A Breast Cancer Heterogeneity from TCGA and GEO Gene Expression Data

Pawan Poudel's study investigates heterogeneity within luminal-A breast cancers using heterocellular gene signatures. The dataset likely contains gene expression profiles and survival data from two independent cohorts: The Cancer Genome Atlas (TCGA, n=817) and GSE42568 (n=104). Data was processed using robust multi-array normalization (RMA) from the R-based Bioconductor package affy.

Use Cases

• Stratifying luminal-A breast cancer samples based on heterocellular subtype signatures mentioned in the description
• Investigating differential therapeutic responses to endocrine therapies like tamoxifen based on gene expression profiles
• Applying colorectal cancer Consensus Molecular Subtypes (CMS) classifiers to breast cancer datasets as described in the methodology
• Performing hypergeometric sample enrichment analysis to understand relationships within the tumor microenvironment

Strengths

• Data is derived from two established, independent breast cancer cohorts: TCGA (n=817) and GSE42568 (n=104)
• Includes corresponding survival data for patient tumors, enabling survival analysis
• Gene expression data was processed using a standardized method (robust multi-array normalization - RMA)

Limitations

• Column-level documentation is absent; field semantics must be inferred after download
• Row count is unknown, which may limit suitability assessment
• Last update date is unknown; freshness unverified

Provenance

Source

Gene Expression Omnibus (GEO) and cBioPortal for TCGA data

Collection Method

Gene expression data downloaded from public repositories (GEO, cBioPortal) and processed with R/Bioconductor packages.